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MLOps: the new role in data science

by Monika Rzepecka , 18.04.2023

Machine learning operations (MLOps) analysts have burst onto the scene as demand has grown among businesses for consistent, reliable insights in-house. Here, one of our MLOps analysts, Monika Rzepecka, shares what the role involves and what it delivers, strategically.

Machine learning operations (MLOps) is a very fresh area. It’s still developing, and companies have differing ideas on what the role is.

Businesses don't even have it yet and are considering incorporating MLOps in the future. In essence, MLOps analysts are a section of the data science team and make machine learning or AI projects more systematic, repeatable, and well-maintained.

Whereas data scientists focus on algorithm design and the interpretation of information, MLOps concentrate on ensuring the model is working at its best amid changing demands. The analysts monitor algorithms metrics, improve performance, and set best practices.

Data science projects typically have the following stages: scoping, collecting data, training the model, and deploying it in production. After that, MLOps comes into play, analysts assess and monitor the viability of the model during use.

For example, as different data are added from new points of sale or customer interfaces, or as the business environment grows. MLOps analysts also have an important reactive role. This occurs when those using the analytics query the results or notice some misbehavior. At that point, they identify the source of the problem and remedy it.  


Benefits of MLOps

While there is not yet a textbook guide to what MLOps should deliver, the core expectation is that we take a systematic approach to machine learning.

The analysts ensure the output is consistent in quality and effectiveness and has transparency and trustworthiness. This applies to both historical and predictive analytics.

There are also significant operating efficiencies that they deliver, by allowing data scientists to focus on their core areas, rather than monitoring and interrogating code usage.

As we draw in a huge amount of data to fuel our business intelligence insights, MLOps is crucial. We have moved from being a traditional market research company to being a trusted provider of prescriptive data analytics, powered by innovative technology.

As with all companies reliant on data science, it is paramount that our data-driven insights are transparent and reliable, and all processes are consistent and efficient. We currently have two full-time MLOps analysts, one position we’re recruiting for right now, and we expect to recruit more.

But, we know this is a relatively new role and people’s skills and backgrounds vary quite widely. My own background is in qualitative methods in economics, working with machine learning algorithms and monitoring tools.

What is important is data curiosity. To be interested in what happens. To be able to drill down into masses of data, spot patterns, and identify what is working or not working.

We also need an understanding of machine learning models and algorithms, and the skills and enthusiasm to investigate and drive important change.


Retaining talent to strengthen the future with machine learning operations

Many MLOps analysts are looking for the ability to grow in the role and make a tangible difference to how the company operates. We are starting with a blank sheet of paper.

We’re the kind of people who thrive on being allowed to innovate to find better ways of working. MLOps analysts like the space to deep dive, and shape the role as the business advances its use of analytics.

Creating this environment of trust and freedom will be important for companies, given that MLOps analysts are in increasing demand.

MLOps will evolve dramatically within different organizations in the coming years, particularly as the appetite for the role grows among business leaders.

There's going to be accelerating growth in what MLOps delivers for different businesses, the smart tools available, and what is possible in collaboration with the data science team. I'm excited to be at the start of this big movement with GfK.

GfK is recruiting! We have various tech roles open and welcome applications from people with or without a tech background. To find out more, check out our careers page!

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